Prognosis stratification of cancer patients treated with immune checkpoint inhibitors through lung immune prognostic index: a meta-analysis and systematic review

Background Although numerous studies have reported the prognostic value of the lung immune prognostic index (LIPI) in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs), the prognostic value of the LIPI in a pancancer setting remains unclear. Methods A comprehensive search was conducted until July 2023 across the PubMed, Embase, Web of Science, and Cochrane Library databases to identify relevant studies evaluating the prognostic value of the LIPI in cancer patients treated with ICIs. The outcomes were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). We described and compared the pooled outcomes by stratifying the patients based on different groupings of LIPI (good vs. intermediate [0 vs. 1], good vs. poor [0 vs. 2], and good vs. intermediate / poor [0 vs. 1 + 2]). Results A total of 9959 patients in 35 studies were included. A higher score of LIPI was associated with impaired OS. The pooled HRs were 1.69 (95% CI: 1.55–1.85, p < 0.001; 0 vs. 1), 3.03 (95% CI: 2.53–3.63, p < 0.001; 0 vs. 2), and 2.38 (95% CI: 1.97–2.88, p < 0.001; 0 vs. 1 + 2). A higher LIPI score was associated with shorter PFS. The pooled HRs were 1.41 (95% CI: 1.31–1.52, p < 0.001; 0 vs. 1), 2.23 (95% CI: 1.87–2.66, p < 0.001; 0 vs. 2), and 1.65 (95% CI: 1.46–1.86, p < 0.001; 0 vs. 1 + 2). Similarly, a higher LIPI score was associated with a lower ORR. The pooled ORs were 0.63 (95% CI: 0.54–0.75, p < 0.001; 0 vs. 1) and 0.38 (95% CI: 0.29–0.50, p < 0.001; 0 vs. 2). A higher LIPI score was associated with a lower DCR. The pooled ORs were 0.47 (95% CI: 0.35–0.61, p < 0.001; 0 vs. 1) and 0.19 (95% CI: 0.12–0.30, p < 0.001; 0 vs. 2). Conclusion In patients with NSCLC or other solid tumours, the lung immune prognostic index could robustly stratify the clinical outcomes into three groups among the patients who receive ICIs. LIPI is a low-cost, simple, accessible, and accurate prognostic tool in a pancancer setting and it may contribute to the evaluation of risk stratification in patients treated with ICIs. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-024-12271-0.


Introduction
Over the past decade, the utilization of immunotherapy has substantially transformed the therapeutic domain of numerous solid tumours [1].Immune checkpoint inhibitors (ICIs) that specifically target programmed cell death-1/programmed death ligand-1 (PD-1/PD-L1) and T-lymphocyte-associated protein 4 (CTLA-4) are particularly noteworthy [2].Immune checkpoint inhibitors (ICIs) have demonstrated notable efficacy in enhancing overall survival rates in various cancer types, such as non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma, and hepatocellular carcinoma (HCC) [3].However, despite considerable achievements, considerable variability in treatment response and survival outcomes is observed among patients undergoing ICI therapy [4], therefore, it is imperative to ascertain suitable biomarkers capable of identifying patients who may not derive substantial benefits from ICI treatment to avert the administration of futile, costly, and potentially harmful interventions [5].
Despite the increasing number of studies investigating prognostic biomarkers in ICI therapy, such as PD-L1 expression, tumour mutational burden (TMB), or mismatch repair deficiency (dMMR) [6][7][8], there is a notable absence of a universally applicable clinical tool.In order to validate these biomarkers, next-generation sequencing (NGS) or immunohistochemical analysis is required [9].However, the biopsy site and specimen status can influence the results.Consequently, there is a need to identify readily accessible biomarkers that are suitable for accurately predicting the efficacy of ICIs treatment across different tumor types in various clinical settings.The immune status of the tumour microenvironment has been shown to be a key indicator of antitumour immune responses [10].Systemic chronic inflammation can dysregulate immune homeostasis and suppress the adaptive antitumour immune response [11].As a representative mediator of systemic inflammation, cancer cell-regulated neutrophils can inhibit the anti-tumor function of T cells, which may hinder the efficacy of immunotherapy [12].Prior studies have indicated the importance of the baseline neutrophil-to-lymphocyte ratio (NLR) and the baseline lactate dehydrogenase (LDH) level in prognostically assessing the outcomes in different types of cancer [13][14][15].LDH is a biomarker of metabolism and proliferation.Serum LDH levels reflect the overall burden of the tumour and reflect its invasiveness [16].Consequently, to strengthen the prognostic power of these two indexes, the lung immune prognostic index (LIPI), which combines the derived NLR (dNLR) and LDH, was proposed as a means of identifying NSCLC patient subgroups with differential tumour responses and survival outcomes after ICI treatment [17,18].This inexpensive and readily available index included a pretreatment dNLR greater than 3 and an LDH level higher than the upper limit of normal, stratifying patients into "poor", "intermediate" and "good" prognostic groups [18].
Recently published studies have highlighted the potential prognostic value of LIPI in solid cancer patients undergoing ICI treatment beyond NSCLC [17,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].Although a considerable number of studies have investigated the association between LIPI and the prognosis of patients treated with ICIs, a comprehensive review on this topic is currently lacking.Hence, we conducted this meta-analysis and systematic review to comprehensively summarize the prognostic importance of the LIPI in patients receiving ICIs in a pancancer setting.

Search strategy
This meta-analysis and systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [34] and the protocol for the analysis was registered prospectively in PROSPERO (CRD42023441536).A comprehensive search of multiple databases including PubMed, Web of Science, the Cochrane Library, and Embase, was conducted to identify relevant studies published until July 2023.The search utilized specific keywords such as "lung immune prognostic index", "LIPI", "cancer", "solid tumour", "tumour", "immunotherapy", "ICI", and "immune checkpoint inhibitor".Furthermore, a manual scan of the references of the included studies was conducted to identify any potentially overlooked studies.

Study selection
The preliminary literature review was conducted by two independent authors (Yusheng Guo and Yao Pan) who identified relevant studies by reading titles and abstracts in various databases.To be considered eligible for inclusion, studies had to meet the following criteria: (1) were published in English, (2) evaluated the prognostic value of LIPI in cancer patients treated with ICIs, and (3) reported outcomes such as overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), or immune-related adverse events (irAEs).

Data extraction
The data extracted from the included studies encompassed various variables, including the year of publication, name of the first author, region, type of ICIs, type of tumour, outcomes, number of enrolled patients, and the ratio of males to females.The assessment of each study was independently conducted by two authors using the Newcastle-Ottawa scale (NOS), with studies scoring an NOS score ≥ 6 classified as high-quality studies.In the event of any disagreements, a resolution was achieved through discussion or consensus with a third author (Xuefeng Kan or Chuansheng Zheng).In instances where multiple publications reported overlapping data, priority was given to the study with the largest sample size or the study with more comprehensive information on LIPI.The primary endpoint of this study was OS, defined as the time from the initiation of treatment to death.The secondary endpoints were as follows:1) PFS, defined as the time from the initiation of treatment to progressive disease (PD) or death; 2) ORR, defined as the proportion of patients with complete response (CR) or partial response (PR); and 3) DCR, defined as the proportion of patients with CR, PR or stable disease (SD).The ancillary endpoint was irAEs.

Statistical analyses
The statistical analysis was performed utilizing R software (version 4.1.0).Prior to conducting a meta-analysis, heterogeneity was assessed through the implementation of a chi-square test and the I 2 metric.The I 2 value serves as an indicator of the proportion of variability across the pooled estimates that can be attributed to statistical heterogeneity.Studies with an I 2 value exceeding 35% were deemed to possess substantial heterogeneity.In instances of high heterogeneity, a random effects model was employed, while a fixed effects model was utilized in cases of low heterogeneity.Subsequently, the forest maps were created, followed by a comprehensive description and discussion of the HRs or ORs along with their corresponding 95% confidence intervals (CIs).Potential sources of heterogeneity were identified utilizing Baujat plots, and sensitivity analyses were subsequently performed by excluding studies one by one.Subgroup analyses of OS and PFS were performed based on patient characteristics.Publication bias was evaluated using funnel plots, Egger's test, and Begg's test.Every time the meta-analysis was conducted with a fixed effect model or a random effect model, a publication bias test was carried out.The results of Egger's test and Begg's test were presented in the table.In cases where publication bias was identified, the trim-and-fill method was employed to generate a model that accounted for such bias.A significance level of p < 0.05 was deemed statistically significant in all the statistical analyses.
Subgroup analyses of OS (0 vs. 1 and 0 vs. 2) were conducted according to retrospective or prospective and tumour type.The results indicated that all subgroups provided similar results (Table S1).Notably, the subgroup analysis of the prospective studies provided similar results to the results of retrospective studies (pooled HR for 0 vs. 1 in retrospective studies: 1.64, 95% CI:
Subgroup analyses were performed (0 vs. 1 and 0 vs. 2) on PFS based on retrospective or prospective design and tumour types.The findings demonstrated consistent outcomes across all subgroups, indicating the stability of the results (Table S2).

Immune-related adverse events (irAEs)
In addition, we investigated studies that reported the association between ancillary endpoint (irAEs) and Fig. 2 Overview of this study LIPI.A total of 2 studies reported by Pierro et al. [23] and Sonehara et al. [48] both showed that low points of LIPI were associated with a high occurrence of irAEs which were fully reported to be related to better clinical outcomes.Pierro et al. reported that they observed a greater rate of irAEs in the good LIPI group, with 17 events (45%) vs. 26 in the intermediate LIPI group (31%) and 13 (30%) in the poor LIPI group.Similarly, Sonehara et al. reported that the development of irAEs was independently predicted by a LIPI score of 0 or 1 (ORR: 0.200, 95% CI: 0.088-0.693,p = 0.011).

Publication bias
Publication bias was evaluated through funnel plots, the Egger's test, and the Begg's test.The funnel plots exhibited approximate symmetry (Figure 1S11A-H and Figure S12 A-B), while the results of the Egger's test and Begg's test indicated the presence of publication bias in the studies examining the relationship between LIPI 1 or 2 and OS (Table 2).The trim-and-fill method identified a need to add six (OS: 0 vs. 1) or five (OS: 0 vs. 2) potential unpublished studies (Figure S12 C-D), and this did not significantly alter the outcome, which yielded pooled HRs of 1.79 (95% CI: 1.64-1.94,p < 0.001; OS: 0 vs. 1) and 3.48 (95% CI: 2.81-4.31,p < 0.001; OS: 0 vs.

Discussion
There is a growing body of evidence regarding the prognostic importance of peripheral blood inflammatory indices in various tumour types and settings [54][55][56].In contrast to measuring biomarkers such as PD-L1, TMB, and MSI, taking routine blood samples offers greater accessibility and does not entail supplementary expenses, making the associated biomarkers readily applicable in Fig. 3 Forest plot for the association between LIPI 1 and OS (blue); the association between LIPI 2 and OS (red) real-world scenarios [5].Therefore, many blood-based biomarkers have been developed for predicting the efficacy of cancer immunotherapy or monitoring the progression of tumours [57][58][59].
Among these biomarkers, the neutrophil-to-lymphocyte ratio (NLR), which reflects the systemic immune response to cancer-related inflammation, a hallmark of the initiation and progression of malignant types of cancer, has been the most studied.From a biological perspective, the NLR serves as an indicator of systemic inflammation and may provide insights into the immune system's equilibrium in the presence of malignancy [54].The neutrophil count is believed to mirror the inflammatory microenvironment, which in turn facilitates tumour-promoting processes such as cancer cell proliferation, metastasis, angiogenesis, and evasion of adaptive immune responses [60].Conversely, lymphocytes possess potent abilities to suppress cancer progression, and their presence, particularly within the tumor   microenvironment, is considered indicative of host immunity [61].The elevation of LDH levels can be attributed to the heightened glycolytic activity of the tumour and tumour necrosis caused by hypoxia, with the latter being correlated with a substantial tumour burden [62].Both glycolysis and hypoxia play a role in fostering an immunosuppressive microenvironment and impair the efficacy of immunotherapy [63].Notably, Mezquita et al. reported that the LIPI score was an immunotherapyspecific prognostic factor and the LIPI could not stratify the prognosis of the chemotherapy cohort [18].Kazandjian et al. then pooled the results of eleven randomized trials and found that LIPI was also a good prognostic predictor in patients with metastatic NSCLC undergoing chemotherapy [17].Subsequently, a considerable number of studies have investigated the prognostic effect of LIPI in nonimmunotherapy patients.For example, LIPI can be used as a prognostic factor in patients with NSCLC or pancreatic cancer receiving radiotherapy, surgery, or tyrosine kinase inhibitors [64,65].Considering that LIPI can play a prognostic role in a variety of tumours and treatment modalities, LIPI may function as a universal prognostic predictor for cancer patients [66].Thus far, this study included 35 studies (40 cohorts), and 9959 patients represented the largest meta-analysis comprehensively summarizing the prognostic value of LIPI in cancer patients treated with ICIs.Although one previous meta-analysis reported the prognostic value of LIPI in cancer patients treated with ICIs [67], it included only 12 studies and 4883 patients.Therefore, they only confirmed that the prognosis was significantly worse in the poor or intermediate LIPI group than in the good LIPI group.The difference in the ability of prognosis stratification between LIPI 1 and LIPI 2 patients remains unclear.In addition, they included studies published in conference abstracts with limited information resulting in limited assessment.A more extensive literature search was performed in this study and we included more cancer patients, accessing more data regarding various tumours and building a good basis for evaluating the prognostic value of LIPI in a pancancer setting.Moreover, Baujat plots and sensitivity analyses were conducted to ascertain the origins of heterogeneity and validate the stability of the obtained outcomes.
Our results represent three findings.First, intermediate or poor LIPI was significantly associated with poor OS, shorter PFS, and worse tumour response in cancer patients treated with ICIs.Specifically, compared to patients with LIPI 0, patients with LIPI 1 had a 1.69-fold greater risk of death, a 1.41-fold greater risk of progression, and a 0.63-fold lower odds of tumour response; similarly, compared to patients with LIPI 0, patients with LIPI 2 had a 3.03-fold greater risk of death, a 2.23-fold greater risk of progression, and a 0.38-fold lower odds of tumour response.Second, the LIPI classification system could robustly stratify the long-term prognosis and short-term treatment efficacy into 3 groups among the cancer patients receiving ICIs.Finally, given that LIPI was developed from NSCLC cohorts, we validated the prognostic value of LIPI in NSCLC patients and non-NSCLC patients.The results indicated that the stratification ability of LIPI was similar in NSCLC patients and non-NSCLC patients.
However, this meta-analysis and systematic review had several limitations.First, some of the included studies were retrospective studies, which led to inevitable selection bias and confounding bias.However, the subgroup analysis of the prospective studies (10 studies and 5110 patients) provided similar results to the overall results or the results of retrospective studies (27 studies and 4849 patients).Second, high heterogeneity was observed in some of the results, but Baujat plots were used to determine the source of heterogeneity.In addition, sensitivity analyses and subgroup analyses validated the stability of the results.Finally, publication bias was observed in some results, however, similar results were achieved with the Trim and Fill method.Despite these limitations, the results of the present study were reliable because low heterogeneity was detected and publication bias was not observed among most of the results.Moreover, with the rapid development of antitumour agents, it is necessary to explore the prognostic ability of LIPI in cancer patients receiving other types of immunotherapies (vaccine, adoptive cell transfer, and cytokine therapy) or antibody-drug conjugates in future studies.In addition, comparative studies for markers of systemic inflammatory responses, including LIPI, Glasgow score, NLR, lymphocyte monocyte ratio (LMR), platelet lymphocyte ratio (PLR), need to be carried out in the future to determine their prognostic role in different solid cancers.

Conclusions
In patients with NSCLC or other solid tumours, the lung immune prognostic index could robustly stratify clinical outcomes into three groups among the patients receiving ICIs.LIPI is a low-cost, simple, accessible, and accurate prognostic tool in a pancancer setting and it may contribute to the evaluation of risk stratification in patients treated with ICIs.

Fig. 1
Fig. 1 Flow diagram of study selection for inclusion in this meta-analysis and systematic review . OS: overall survival; PFS: progression-free survival; ORR: objective response rate; DCR: disease control rate

Fig. 4
Fig.4 Forest plot for the association between LIPI 1 and PFS (blue); the association between LIPI 2 and PFS (red)

Table 2
Publication bias